From 15d0baa45926de9a132df56b7bfa8106417b2f22 Mon Sep 17 00:00:00 2001
From: "Abdul Qureshi (quresh40)" <quresh40@coventry.ac.uk>
Date: Thu, 9 Dec 2021 14:00:26 +0000
Subject: [PATCH] copying from old

i got a new laptop and trying to recover everything from it since i didnt use github
---
 .DS_Store                                     | Bin 6148 -> 8196 bytes
 .vscode/settings.json                         |   3 +
 6006CW.ipynb                                  |  89 +++++++++++-------
 .../~$06CEM-Coursework Template 8581640.docx  | Bin 0 -> 162 bytes
 4 files changed, 59 insertions(+), 33 deletions(-)
 create mode 100644 .vscode/settings.json
 create mode 100644 Report/~$06CEM-Coursework Template 8581640.docx

diff --git a/.DS_Store b/.DS_Store
index b76b27439844626d956e50b77b950a75aa2fa65a..d66370aefa294db41259e7734658528267c0c3bc 100644
GIT binary patch
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delta 458
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diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..625b81c
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "jupyter.jupyterServerType": "local"
+}
\ No newline at end of file
diff --git a/6006CW.ipynb b/6006CW.ipynb
index 21ec5b6..f0936a3 100644
--- a/6006CW.ipynb
+++ b/6006CW.ipynb
@@ -2,21 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'pandas'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/ll/tlqktfdj5lsbw30921tmh1p40000gn/T/ipykernel_2669/3985515910.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -28,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +337,7 @@
        "14      9.2        5  "
       ]
      },
-     "execution_count": 93,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -363,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +372,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,22 +384,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7f83d541c9d0>"
+       "<seaborn.axisgrid.FacetGrid at 0x7fcc7ada03a0>"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -430,7 +418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -439,13 +427,13 @@
        "<AxesSubplot:xlabel='quality', ylabel='free sulfur dioxide'>"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -457,7 +445,7 @@
     },
     {
      "data": {
-      "image/png": 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kzUufRXkAsHHK9EQ3b1v+DfD5HvNI0rws73HbmWFezbhi8ksMivIXt7H8TOBMgIMPPnih8knSUPrco5wADpoyfSDw4PSVkhwOXAKcXFXfmmlDVXVxVa2qqlUrVqzoJawkbUufRXkTcGiSQ5LsBpwCXD11hSQHA1cBp1XV/+kxiyTNW2+H3lW1Jck5wHXAMuDSqrojyVnd8rXAnwA/BXwkCcCWqlrVVyZJmo8+z1FSVeuAddPmrZ3y/u3A2/vMIEnbyztzJKnBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhosSklqsCglqcGilKQGi1KSGixKSWqwKCWpwaKUpAaLUpIaLEpJarAoJanBopSkBotSkhp6LcokJya5O8mGJOfNsDxJPtQt/0aSI/vMI0nz0VtRJlkGfBg4CTgMODXJYdNWOwk4tPs5E/hoX3kkab763KM8CthQVfdU1ZPAFcDJ09Y5Gbi8Br4C7Jvk+T1mkqQ567MoDwA2Tpme6ObNdR1JGqnlPW47M8yreaxDkjMZHJoDPJ7k7u3Mti37AY8sxIZywdsWYjNzsWDZee9M/1p6s3B/5u9c1NywkH/mWZp/5u/4LwuxlTlZsOx//Mln/Zn/7LbW7bMoJ4CDpkwfCDw4j3WoqouBixc64HRJ1lfVqr4/pw9LNftSzQ1LN/tSzQ2jy97nofdNwKFJDkmyG3AKcPW0da4GTu++/T4a+HZVPdRjJkmas972KKtqS5JzgOuAZcClVXVHkrO65WuBdcDrgA3AE8AZfeWRpPnq89CbqlrHoAynzls75X0BZ/eZYY56P7zv0VLNvlRzw9LNvlRzw4iyZ9BVkqRt8RZGSWqwKIEkuyf5WpKvJ7kjyfmjzjQXSZYl+fsknxt1lrlIcl+S25LcmmT9qPPMRZJ9k1yZ5JtJ7kryilFnaknywu7PeuvPd5K8e9S5hpHk3O7/zduTfCrJ7ov6+R56D+45B/aqqseT7Ar8L+Bd3d1CYy/JvwVWAftU1etHnWdYSe4DVlXVwlyLuIiSfBz4UlVd0l3VsWdVPTbiWEPrbjHeBLy8qu4fdZ7ZJDmAwf+Th1XV95P8JbCuqj62WBnco2TwpVJVPd5N7tr9LIm/QZIcCPwqcMmos+wskuwDHAv8OUBVPbmUSrJzAvB/x70kp1gO7JFkObAnM1xv3SeLstMdvt4KPAz8z6r66ogjDeuDwGrg6RHnmI8C/ibJzd3dV0vFzwGbgcu6Ux6XJNlr1KHm6BTgU6MOMYyq2gRcADwAPMTgeuu/WcwMFmWnqp6qqhczuDvoqCT/YsSRmpK8Hni4qm4edZZ5OqaqjmQwitTZSY4ddaAhLQeOBD5aVS8Bvgc8axjBcdWdKngj8FejzjKMJM9lMIDOIcDPAHsleetiZrAop+kOoa4HThxtkqEcA7yxO9d3BfDqJJ8YbaThVdWD3evDwGcYjDi1FEwAE1OOOq5kUJxLxUnALVX1j6MOMqRfBu6tqs1V9UPgKuCVixnAogSSrEiyb/d+Dwb/Yr450lBDqKp/V1UHVtVKBodSX6iqRf2bdr6S7JVk763vgV8Bbh9tquFU1SSwMckLu1knAHeOMNJcncoSOezuPAAcnWTP7ovXE4C7FjNAr3fmLCHPBz7efRO4C/CXVbWkLrVZgp4HfGbw3z3Lgb+oqmtHG2lO3gF8sjuMvYclcvttkj2B1wC/O+osw6qqrya5ErgF2AL8PYt8h46XB0lSg4fektRgUUpSg0UpSQ0WpSQ1WJSS1GBRaoeXZGWS27v3q5J8qHt/fJJFvXBZS5PXUWqnUlXrga1Duh0PPA7cOLJAWhLco9RYS/KeJHcn+dtuHMI/SHJ9klXd8v26Wzi37jl+Kckt3c+z9ha7vcjPJVkJnAWc243N+Kok93bD7JFkn268zF0X759W48o9So2tJC9lcGvmSxj8t3oLMNsAIA8Dr6mqHyQ5lMFtejM+2rSq7kuyFni8qi7oPu96BkPW/XX3uZ/u7i3WTs49So2zVwGfqaonquo7PPtxx9PtCvy3JLcxGBnnsDl+3iX8+FbEM4DL5vj72kG5R6lxN9M9tlv48V/yUx8JcC7wj8AR3fIfzOmDqr7cHb4fByyrqiUxSIf65x6lxtkNwK8l2aMbaegN3fz7gJd27988Zf3nAA9V1dPAaQyeJz+b7wJ7T5t3OYNDdvcm9SMWpcZWVd0C/A/gVuDTwJe6RRcAv5fkRmC/Kb/yEeBtSb4CvIDBgLqzuYZBEd+a5FXdvE8Cz2VpDUOmnjl6kJaMJP+BKV++9PQZbwZOrqrT+voMLT2eo5Q6SS5kMPr360adRePFPUpJavAcpSQ1WJSS1GBRSlKDRSlJDRalJDVYlJLU8P8BcmWZVIdjFUwAAAAASUVORK5CYII=",
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"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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -469,7 +457,7 @@
     },
     {
      "data": {
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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -481,7 +469,7 @@
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -493,7 +481,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 360x360 with 1 Axes>"
       ]
@@ -528,21 +516,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X= df.iloc[:, :-1].values\n",
-    "y= df.iloc[:,-1].values\n",
+    "#looking at the data it seems they the lowest in terms of quality is 3 and hiest is 8 so we make bins(replace data to help representation)\n",
+    "# im gonna replace 3-4 to be low (0) 5-7 to be medium(1) and 8-9(2) its hard since theyre an odd amount of numbers and theyre all whole numbers so no real \n",
+    "bins = [0, 4.5, 7.5, 10] \n",
+    "labels = [0, 1, 2]\n",
+    "#cut() lets us do this\n",
+    "df['quality'] = pd.cut(df['quality'], bins=bins, labels=labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X= df.iloc[:, :-1].values #all rows except last column\n",
+    "y= df.iloc[:,-1].values #all rows and last column\n",
     "#split the set into two"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[\"citric acid\"]\n",
+    "y = df[\"quality\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.)\n",
+    "#"
+   ]
   }
  ],
  "metadata": {
@@ -564,7 +587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.7"
   },
   "orig_nbformat": 4
  },
diff --git a/Report/~$06CEM-Coursework Template 8581640.docx b/Report/~$06CEM-Coursework Template 8581640.docx
new file mode 100644
index 0000000000000000000000000000000000000000..7cd2720d17f65bd273245041249827980a66a4e5
GIT binary patch
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zHEC5<QCCyYuA!uj?VoP2jAJCElWDLY-mHauLEF&|?gA}?s;(~|prMBkH_5IS?(-Z+

literal 0
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